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A Case Study on the Effectiveness of Bilingual Instructors Compared with Monolingual Instructors at a Private University in Saudi Arabia

2022· article· en· W4220965635 on OpenAlexaff
Ziad ElJishi, Terumi Taylor, Heba Shehata

Bibliographic record

VenueInternational journal of education and literacy studies · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPsychologyArabicMathematics educationPreferenceNeuroscience of multilingualismBilingual educationObservational studyMedium of instructionSemitic languagesPedagogyLinguistics

Abstract

fetched live from OpenAlex

The purpose of the study was to compare the instructional effectiveness of bilingual instructors compared to monolingual instructors. The case study design was non-experimental using a mixed methods approach. The data was collected from surveys, interviews, and classroom observations of monolingual and bilingual instructors (n=120) at a private university in Saudi Arabia. The survey and interview results showed bilingual instructors in favor of the bilingual method while monolingual instructors were not. Classroom observational data showed more incidents of student engagement recorded in the bilingual instruction compared to the classrooms where monolingual instruction took place. The implications of the study demonstrate the need for a policy change to allow bilingual instruction in the classroom and for preference given to the hiring of bilingual instructors as a means to facilitate student understanding of concepts in the classroom and in empowering second language acquisition of native Arabic speaking students.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.032
GPT teacher head0.307
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2022
Admission routes1
Has abstractyes

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